Article ID: | iaor20041241 |
Country: | Germany |
Volume: | 95 |
Issue: | 2 |
Start Page Number: | 249 |
End Page Number: | 277 |
Publication Date: | Jan 2003 |
Journal: | Mathematical Programming |
Authors: | Roos C., Terlaky T., Andersen E.D. |
Keywords: | duality, interior point methods |
Based on the work of the Nesterov and Todd on self-scaled cones an implementation of a primal–dual interior-point method for solving large-scale sparse conic quadratic optimization problems is presented. The main features of the implementation are that it is based on a homogeneous and self-dual model, it handles rotated quadratic cones directly, it employs a Mehrotra type predictor–corrector extension and sparse linear algebra to improve the computational efficiency. Finally, the implementation exploits fixed variables which naturally occurs in many conic quadratic optimization problems. This is a novel feature for our implementation. Computational results are also presented to document that the implementation can solve very large problems robustly and efficiently.